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Record W1970079157 · doi:10.1139/l00-018

A three-dimensional finite element model for locally reinforced timber joints made with hollow dowel fasteners

2000· article· en· W1970079157 on OpenAlexvenueno aff
Zhongwei Guan, P.D. Rodd

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2000
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research Council
KeywordsDowelFastenerStructural engineeringFinite element methodStiffnessFailure mode and effects analysisJoint (building)BrittlenessOrthotropic materialMaterials scienceEmbedmentComposite materialEngineering

Abstract

fetched live from OpenAlex

Brittle premature failure caused by splitting parallel to the timber grain is a common failure mode in glulam joints made with solid dowel type fasteners. It is thought that this problem can be alleviated by using hollow steel dowels as the fasteners and reinforcing the timber locally in the area of the joint. In this way, by varying the wall thickness of the dowels and the thickness of the reinforcing members, a chosen combination of strength and stiffness should be attainable together with a ductile failure mode. In this paper, three-dimensional nonlinear finite element models are developed to simulate (i) the structural performance of a timber joint made with a single hollow steel dowel and (ii) a moment transmitting joint made with a number of the dowels, each type being locally reinforced by densified veneer wood. The models incorporate suitably defined elastoplasticity and orthotropic elasticity and also allow for large deformations of the joints as well as for frictional contact between the timber and the dowel. They are calibrated against physical test data from joints loaded to failure.Key words: glulam, densified veneer wood, resin injected, hollow dowel fastener, moment transmitting, ductile failure, finite element.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.572
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.165
Teacher spread0.154 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2000
Admission routes1
Has abstractyes

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